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fix: correct online rollout lifecycle

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ViperEkura
2026-07-23 19:01:37 +08:00
parent 99b5d2b2da
commit 8ab5631446
7 changed files with 173 additions and 20 deletions
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astrai/dataset/dataset.py
+6 -2
View File
@@ -190,7 +190,8 @@ def grpo_collate_fn(batch: List[Dict[str, Tensor]]) -> Dict[str, Tensor]:
- rewards: [G]
Output:
- prompts: [B, P_max]
- prompts: [B, P_max], left-padded
- prompt_mask: [B, P_max]
- responses: [B, G, R_max]
- masks: [B, G, R_max]
- rewards: [B, G]
@@ -201,13 +202,15 @@ def grpo_collate_fn(batch: List[Dict[str, Tensor]]) -> Dict[str, Tensor]:
R_max = max(r.size(0) for b in batch for r in b["responses"])
prompts = torch.zeros(B, P_max, dtype=torch.long)
prompt_mask = torch.zeros(B, P_max, dtype=torch.bool)
responses = torch.zeros(B, G, R_max, dtype=torch.long)
masks = torch.zeros(B, G, R_max, dtype=torch.bool)
rewards = torch.zeros(B, G, dtype=torch.float32)
for i, b in enumerate(batch):
p_len = b["prompts"].size(0)
prompts[i, :p_len] = b["prompts"]
prompts[i, -p_len:] = b["prompts"]
prompt_mask[i, -p_len:] = True
rewards[i, : b["rewards"].size(0)] = b["rewards"]
for g in range(min(G, len(b["responses"]))):
r_len = b["responses"][g].size(0)
@@ -217,6 +220,7 @@ def grpo_collate_fn(batch: List[Dict[str, Tensor]]) -> Dict[str, Tensor]:
return {
"prompts": prompts,
"prompt_mask": prompt_mask,
"responses": responses,
"masks": masks,
"rewards": rewards,
astrai/trainer/rollout.py
+88 -12
View File
@@ -35,6 +35,7 @@ class RawRollout:
Fields:
prompts: Tokenized prompts, shape ``[B, P_len]``.
prompt_mask: Boolean mask for real prompt tokens, shape ``[B, P_len]``.
responses: Generated response token IDs, shape ``[B, G, R_max]``.
response_mask: Boolean mask for real (non-pad) response tokens,
shape ``[B, G, R_max]``.
@@ -47,6 +48,7 @@ class RawRollout:
"""
prompts: Tensor
prompt_mask: Tensor
responses: Tensor
response_mask: Tensor
logprobs_old: Tensor
@@ -143,6 +145,15 @@ class RolloutGenerator:
``add_generation_prompt=True`` so rollout prompts match the
format the policy was SFT-trained on.
"""
model = self.scheduler._executor.model
was_training = model.training
model.eval()
try:
return self._generate_eval(batch)
finally:
model.train(was_training)
def _generate_eval(self, batch: Dict) -> RawRollout:
prompt_texts, flat_prompt_ids = self._prepare_prompts(batch)
B = len(prompt_texts)
G = self.group_size
@@ -161,6 +172,15 @@ class RolloutGenerator:
rep_window=self.rep_window,
return_logprobs=True,
)
if len(results) != B * G:
raise RuntimeError(
f"Rollout scheduler returned {len(results)} results, expected {B * G}"
)
for token_ids, logprobs in results:
if len(token_ids) != len(logprobs):
raise RuntimeError(
"Rollout scheduler returned misaligned token IDs and logprobs"
)
# Each element is (token_ids, logprobs); pad to max length.
max_len = 0
@@ -171,10 +191,12 @@ class RolloutGenerator:
device = self.scheduler.device
P_len = max(len(ids) for ids in flat_prompt_ids)
prompts_tensor = torch.zeros(B, P_len, dtype=torch.long, device=device)
prompt_mask = torch.zeros(B, P_len, dtype=torch.bool, device=device)
for i, ids in enumerate(flat_prompt_ids):
prompts_tensor[i, : len(ids)] = torch.tensor(
prompts_tensor[i, -len(ids) :] = torch.tensor(
ids, dtype=torch.long, device=device
)
prompt_mask[i, -len(ids) :] = True
responses = torch.full((B, G, max_len), _PAD, dtype=torch.long, device=device)
response_mask = torch.zeros((B, G, max_len), dtype=torch.bool, device=device)
@@ -201,6 +223,7 @@ class RolloutGenerator:
return RawRollout(
prompts=prompts_tensor,
prompt_mask=prompt_mask,
responses=responses,
response_mask=response_mask,
logprobs_old=logprobs_old,
@@ -239,17 +262,35 @@ class RolloutGenerator:
f"{list(batch.keys())}"
)
prompt_texts: List[str] = []
flat_prompt_ids: List[List[int]] = []
for messages in messages_list:
text = self.tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
try:
prompt_texts = self.tokenizer.apply_chat_template(
messages_list, tokenize=False, add_generation_prompt=True
)
ids = self.tokenizer.apply_chat_template(
messages, tokenize=True, add_generation_prompt=True
)
prompt_texts.append(text)
flat_prompt_ids.append(list(ids))
if (
not isinstance(prompt_texts, list)
or len(prompt_texts) != len(messages_list)
or not all(isinstance(text, str) for text in prompt_texts)
):
raise TypeError("Tokenizer does not support batched chat templates")
flat_prompt_ids = self.tokenizer.encode(prompt_texts)
if len(flat_prompt_ids) != len(messages_list) or not all(
isinstance(ids, list) for ids in flat_prompt_ids
):
raise TypeError("Tokenizer does not support batched encoding")
except (TypeError, IndexError, KeyError):
# Keep compatibility with lightweight tokenizer adapters that only
# implement the single-conversation template API.
prompt_texts = []
flat_prompt_ids = []
for messages in messages_list:
text = self.tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
ids = self.tokenizer.apply_chat_template(
messages, tokenize=True, add_generation_prompt=True
)
prompt_texts.append(text)
flat_prompt_ids.append(list(ids))
return prompt_texts, flat_prompt_ids
@staticmethod
@@ -308,6 +349,7 @@ class RolloutRunner:
self.rollout_interval = rollout_interval
self._cache: Optional[RolloutResult] = None
self._cache_key = None
self._steps_since_rollout: int = 0
def step(self):
@@ -317,12 +359,40 @@ class RolloutRunner:
def clear_cache(self):
"""Force next call to re-run rollout."""
self._cache = None
self._cache_key = None
@staticmethod
def _batch_key(batch: Dict):
"""Build a stable key for the prompt fields accepted by the generator."""
def freeze(value):
if isinstance(value, dict):
return tuple(sorted((key, freeze(val)) for key, val in value.items()))
if isinstance(value, (list, tuple)):
return tuple(freeze(item) for item in value)
return value
fields = ("messages", "instruction", "input", "output")
return tuple(
(field, freeze(batch[field])) for field in fields if field in batch
)
def _score(self, raw: RawRollout) -> RolloutResult:
rewards = self.reward_model.score(raw.prompt_texts, raw.response_texts)
if not isinstance(rewards, Tensor):
rewards = torch.as_tensor(rewards, dtype=torch.float32)
expected_shape = raw.responses.shape[:2]
if rewards.shape != expected_shape:
raise ValueError(
f"Reward model returned shape {tuple(rewards.shape)}, "
f"expected {tuple(expected_shape)}"
)
if not torch.isfinite(rewards).all():
raise ValueError("Reward model returned non-finite values")
device = raw.prompts.device
return RolloutResult(
prompts=raw.prompts,
prompt_mask=raw.prompt_mask,
responses=raw.responses,
response_mask=raw.response_mask,
rewards=rewards.to(device=device),
@@ -337,9 +407,15 @@ class RolloutRunner:
Triggers a new rollout when ``_steps_since_rollout >= rollout_interval``
or when the cache is empty.
"""
if self._cache is None or self._steps_since_rollout >= self.rollout_interval:
cache_key = self._batch_key(batch)
if (
self._cache is None
or cache_key != self._cache_key
or self._steps_since_rollout >= self.rollout_interval
):
raw = self.generator.generate(batch)
self._cache = self._score(raw)
self._cache_key = cache_key
self._steps_since_rollout = 0
return self._cache, True
return self._cache, False
astrai/trainer/strategy.py
+15 -3
View File
@@ -158,6 +158,11 @@ class BaseStrategy(ABC):
"""
pass
def on_optimizer_step(self):
"""Advance online rollout state after a successful optimizer step."""
if self._rollout_runner is not None:
self._rollout_runner.step()
def __call__(self, batch: Dict[str, Tensor]) -> Tensor:
"""Run offline or online forward depending on runner injection."""
if self._rollout_runner is None:
@@ -166,8 +171,6 @@ class BaseStrategy(ABC):
result, is_fresh = self._rollout_runner(batch)
if is_fresh:
self._on_rollout_refresh()
if self.executor and self.executor.sync_gradients:
self._rollout_runner.step()
train_batch = self.prepare_from_rollout(result)
return self.compute_loss(train_batch)
@@ -411,6 +414,12 @@ class GRPOStrategy(BaseStrategy):
responses_flat = responses.view(-1, response_len)
masks_flat = masks.view(-1, response_len)
prompt_expanded = prompts.unsqueeze(1).repeat(1, group_size, 1).flatten(0, 1)
prompt_mask = batch.get("prompt_mask")
if prompt_mask is None:
prompt_mask = prompts.ne(0)
prompt_mask_expanded = (
prompt_mask.unsqueeze(1).expand(-1, group_size, -1).flatten(0, 1)
)
prompt_len = prompt_expanded.size(1)
full_sequences = torch.cat([prompt_expanded, responses_flat], dim=-1)
@@ -423,7 +432,9 @@ class GRPOStrategy(BaseStrategy):
)
# Build full attention mask: key-padding + causal
key_pad = full_sequences.bool()[:, None, None, :]
key_pad = torch.cat([prompt_mask_expanded, masks_flat.bool()], dim=-1)[
:, None, None, :
]
S = key_pad.shape[-1]
causal = torch.tril(
torch.ones(S, S, dtype=torch.bool, device=full_sequences.device)
@@ -485,6 +496,7 @@ class GRPOStrategy(BaseStrategy):
def prepare_from_rollout(self, result: RolloutResult) -> Dict[str, Tensor]:
return {
"prompts": result.prompts,
"prompt_mask": result.prompt_mask,
"responses": result.responses,
"masks": result.response_mask,
"rewards": result.rewards,
astrai/trainer/trainer.py
+1
View File
@@ -83,6 +83,7 @@ class Trainer:
if executor.sync_gradients:
self._call_callbacks("on_optimizer_step", context)
context.optimizer.step()
context.strategy.on_optimizer_step()
context.optimizer.zero_grad()
if context.scheduler: